Final Project

library(tidyverse)
# Load the necessary libraries
library(rnaturalearth)
library(rnaturalearthdata)
library(ggplot2)
library(dplyr)
library(ggthemes)

## Warning: package 'ggthemes' was built under R version 4.3.3

library(plotly)

## Warning: package 'plotly' was built under R version 4.3.3

Exercise 1

Insert any text here.

# Insert code for Exercise 1 here
#| crop = true

# Get the world map data
world_map <- ne_countries(scale = "medium", returnclass = "sf") |>
  filter(name != "Antarctica")
renewable_grow <- read.csv("annual-percentage-change-renewables.csv") |>
  filter(Code != "NA") 

renewable_grow$Renewables....growth. <- round(renewable_grow$Renewables....growth.,2)

renewable_plot <- function(region, year) {
renewable_grow22 <- renewable_grow |>
  filter(Year == year)

renewable_grow_map22 <- left_join(world_map,renewable_grow22,
                                
                                c( "iso_a3_eh"= "Code")) |>
rename( "growth" = "Renewables....growth.")

if (region == "World") {
  
} else if (region == "North America" | region == "Latin America & Caribbean") {
  renewable_grow_map22 <- filter(renewable_grow_map22, region_wb == region)
}
  else {renewable_grow_map22 <- filter(renewable_grow_map22, region_un == region )}

renewable_grow_map22 |>
  mutate(text = paste("<b>",admin,"<b>\n growth rate:",signif(growth,2)))

 p <- ggplot(data = renewable_grow_map22) +
 geom_sf( aes( fill = growth + runif(nrow(renewable_grow_map22),min = 0, max = 0.001), text = paste("<b>",admin,"<b>\n growth rate:",signif(growth,2))), color = "black")+
  #scale_fill_manual("", values = my_colors)+
 labs(
   title = paste("Annual percentage change in renewable energy generation in", year),
   fill = "Growth %") +
  scale_fill_continuous(low = "red", high = "blue", limits = c(-20, 20), guide = guide_colorbar(direction = "horizontal", ticks = TRUE, nbin = 50, barwidth = 10, barheight = 1, label.position = "bottom", title.position = "top", title.hjust = 0.5)) +
 theme_map() 
#scale_fill_continuous(low = "red", mid = "white", high = "blue", labels = scales::percent)

 ggplotly(p, tooltip = "text") |>
   style(hoveron = "fills")

}

renewable_plot("World", 2022)

## Warning in layer_sf(geom = GeomSf, data = data, mapping = mapping, stat = stat,
## : Ignoring unknown aesthetics: text

Exercise 2

energy_gen <- read.csv("modern-renewable-prod.csv") 

egen_plot <- function(region) {
  energy_gen_spec_line <- energy_gen |>
    filter(Entity == region) |>
    rename( "Wind"="Electricity.from.wind...TWh" ,"Hydro" = "Electricity.from.hydro...TWh", "Solar" = "Electricity.from.solar...TWh","Other"  = "Other.renewables.including.bioenergy...TWh")
  energy_gen_spec_point <- energy_gen_spec_line |>
    pivot_longer(cols = -c(Entity, Code, Year), names_to = "sources",
                 values_to = "Energy" )
  
  p <- ggplot()  +
    geom_line(data = energy_gen_spec_line,aes(x = Year, y = Wind), color = "purple") + 
    geom_line(data = energy_gen_spec_line,aes(x = Year, y = Hydro), color = "pink") +
    geom_line(data = energy_gen_spec_line,aes(x = Year, y = Solar), color = "green") +
    geom_line(data = energy_gen_spec_line,aes(x = Year, y = Other), color = "orange") +
    geom_point(data = energy_gen_spec_point, aes(x = Year, y = Energy, color = sources), size = 2)
  
  ggplotly(p, tooltip = c("Energy","Year"))
    
  
}

egen_plot("North America")

…